Abstract：Traditional principal analysis method(PCA)based on linear transform is effective method of seismic attribute dimension-reducing optimization.However,the principle component detected by PCA method can't reflect the non-linear attributes if there exists non-linear attribute in raw data.The KPCA is non-linear transform based on the raw data,which can detect the non-linear relationship between the data.Starting from the principle description of the method,the paper analyzed the shortcomings existing in handling the non-linear issue by common PCA,expounded the PCA method based on kernel function,and used the method for the dimension-reducing optimization of seismic attribute for the first time.The application showed the PCA method based on kernel function has wonderful character-detected property.
